Snowflake Snowflake Certification GES-C01

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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data engineer is designing an automated data pipeline in Snowflake to process incoming customer feedback documents. The pipeline needs to perform the following steps: 1. Extract the overall sentiment from the feedback text. 2. Generate a concise summary of each feedback document. 3. Extract key entities (e.g., product, issue, customer name) into a structured JSON format using a powerful LLM, ensuring adherence to a predefined schema and graceful error handling. Which of the following Snowflake Cortex features and best practices should the data engineer leverage to build this robust AI-infused pipeline?

A) Option A
B) Option D
C) Option C
D) Option B
E) Option E

2. An organization is building a new knowledge base system within Snowflake, which relies on 'SNOWFLAKE.CORTEX.EMBED_TEXT_1024' to generate and store embeddings for documents in a 'VECTOR(FLOAT, 1024)' column. They plan to use these embeddings for semantic search and integrate them into various data processing workflows. Which of the following statements accurately describe limitations or specific compatibility aspects of 'EMBED TEXT 1024' or the 'VECTOR' data type within Snowflake?

A) When 'EMBED_TEXT 1024' is invoked within a Snowflake dynamic table's SELECT statement, it allows for continuous, automated updates of embeddings as new data arrives.
B) The 'VECTOR data type is not supported in 'VARIANT columns, which means direct storage of embeddings alongside other semi-structured metadata in a single "VARIANT column is not possible.
C) To compare the generated 1024-dimension vectors for similarity, only the 'VECTOR COSINE SIMILARITY function is officially supported by Snowpark Python.
D) The 'EMBED function in the Cortex REST API can be used to process a list of text strings, where each individual string can be up to 4096 characters long.
E) The 'VECTOR data type, used to store the output of is fully supported as primary or secondary index keys in Snowflake's hybrid tables.

3. A new ML Engineer, 'data_scientist_role' , has been assigned to a project involving custom machine learning models in Snowflake. They need to gain the necessary permissions to perform the following actions related to Snowflake Model Registry and Snowpark Container Services: 1. Log a custom model into a specified schem a. 2. Deploy that model to an existing Snowpark Container Service compute pool. 3. Call the deployed model for inference using SQL. Which of the following SQL commands grant the 'minimal' required privileges to the for these actions, assuming the compute pool and image repository already exist and are appropriately configured?

A)

B)

C)

D)

E)

4. An ML engineer is deploying a custom PyTorch-based image classification model, obtained from Hugging Face, to Snowpark Container Services (SPCS). The deployment requires GPU acceleration on a compute pool named 'my_gpu_pool' and specific Python packages ('torch' , 'transformers' , 'opencv-python'). The scenario dictates that 'opencv-python' is only available via PyPl, while 'torch' and 'transformers' can be sourced from either conda-forge or PyPl. The engineer uses the Snowflake Model Registry to log the model. Which of the following and configurations correctly specify the necessary Python dependencies and GPU utilization for this inference service, adhering to Snowflake's recommendations?

A)

B)

C)

D)

E)

5. A financial analyst is concerned about the rising costs of their Document AI pipeline, which uses 'invoice_model!PREDlCT' to extract data from daily financial reports. They observe that their assigned 'LARGE virtual warehouse is running continuously, even during periods of low document ingestion, contributing significantly to their bill. They want to investigate how to reduce costs effectively for their existing Document AI setup.

A) Option A
B) Option D
C) Option C
D) Option B
E) Option E

Solutions: